Artificial neural network combining sensory signal classification and image generation

ABSTRACT

A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.

BACKGROUND

The invention relates to the field of artificial intelligence (AI) inmedical imaging.

Traditionally, medical images are reviewed and analyzed by human expertssuch as radiologists, doctors, and technicians. Today, with the adventof AI technology, analysis of medical images is becoming increasinglyautomated, with AI algorithms being able to reliably perform tasks suchas area segmentation, parameter measurement, pathology detection, andeven diagnosis of various medical conditions. The use of AI in medicalimagery analysis increases productivity, helps standardize processes atthe medical facility, and often improves diagnosis accuracy.

Since the majority of medical images acquired today are still beingmanually reviewed by experts, many existing medical imaging devices,such as X-Ray machines, CT (Computerized Tomography) and MRI (MagneticResonance Imaging) scanners, ultrasound imagers, etc., perform imageprocessing adjustments of the raw signals they acquire in order to makethe resulting images more suited for human review. For example, manyimaging devices perform automated brightness, contrast, sharpness,and/or other adjustments to the raw signal, so that the output imagehighlights clinically-important features—be it soft tissue, hard tissue,tumors, blood vessels, or other structures and textures. Some modernimaging devices also let their users define the medical imaging scenarioor goal, and apply a different set of image processing adjustments basedon the scenario of interest.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent to those of skill inthe art upon a reading of the specification and a study of the figures.

SUMMARY

The following embodiments and aspects thereof are described andillustrated in conjunction with systems and methods which are meant tobe exemplary and illustrative, not limiting in scope.

One embodiment relates to a method comprising operating at least onehardware processor to: Obtain a training set which comprises: multipledata pairs each comprising: (i) a raw sensory signal acquired by amedical imaging system, and (ii) a processed image generated by themedical imaging system from the raw sensory signal; and a classificationlabel for each of the data pairs. Based on the training set, train anartificial neural network (ANN), wherein the training comprisesminimizing a global loss which is a weighted sum of: a loss between theclassification labels and classification predictions by the ANN, and asimilarity loss between the processed images and images generated by anintermediate layer of the ANN. The trained ANN is thus configured, for anew raw sensory signal: to predict a new classification, and to generatea new image by the intermediate layer of the ANN.

Another embodiment relates to a system which comprises at least onehardware processor, and a non-transitory computer-readable storagemedium having program code embodied therewith, the program codeexecutable by said at least one hardware processor to: Obtain a trainingset which comprises: multiple data pairs each comprising: (i) a rawsensory signal acquired by a medical imaging system, and (ii) aprocessed image generated by the medical imaging system from the rawsensory signal; and a classification label for each of the data pairs.Based on the training set, train an artificial neural network (ANN),wherein the training comprises minimizing a global loss which is aweighted sum of: a loss between the classification labels andclassification predictions by the ANN, and a similarity loss between theprocessed images and images generated by an intermediate layer of theANN. The trained ANN is thus configured, for a new raw sensory signal:to predict a new classification, and to generate a new image by theintermediate layer of the ANN.

In some embodiments, the method further comprises, or the program codeis further executable by said at least one hardware processor, to:acquire the new raw sensory signal by another medical imaging system;and apply the trained ANN to the new raw sensory signals, to: predictthe new classification for the new raw sensory signal, and generate thenew image from the new raw sensory signal.

In some embodiments, the training set further comprises manualsegmentations of distinct areas for at least some of the processedimages; and the training is further to segment distinct areas in atleast some of the generated new images.

In some embodiments, the distinct areas each represent at least one of:a pathology and an anatomical structure.

In some embodiments, the ANN comprises a deep neural network (DNN).

In some embodiments, the ANN comprises a generative adversarial network(GAN).

A further embodiment relates to a different system, which comprises atleast one hardware processor, and a non-transitory computer-readablestorage medium having program code embodied therewith, the program codeexecutable by said at least one hardware processor to: (a) acquire a newraw sensory signal by a medical imaging system, and (b) apply anartificial neural network (ANN) to the new raw sensory signal, to:predict a new classification for the new raw sensory signal, andgenerate, by an intermediate layer of the ANN, a new image from the newraw sensory signal.

In some embodiment of the different system: The ANN was or is trainedbased on a training set which comprises: multiple data pairs eachcomprising: (i) a raw sensory signal acquired by another medical imagingsystem, and (ii) a processed image generated by the other medicalimaging system from the raw sensory signal; and a classification labelfor each of the data pairs. The training of the ANN comprised orcomprises minimizing a global loss which is a weighted sum of: a lossbetween the classification labels and classification predictions by theANN, and a similarity loss between the processed images and imagesgenerated by the intermediate layer of the ANN.

In some embodiments of the different system, the training set furthercomprises manual segmentations of distinct areas for at least some ofthe processed images; and the training is further to segment distinctareas in at least some of the generated new images.

In some embodiments of the different system, the distinct areas eachrepresent at least one of: a pathology and an anatomical structure.

In some embodiments of the different system, the ANN comprises a deepneural network (DNN).

In some embodiments of the different system, the ANN comprises agenerative adversarial network (GAN).

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thefigures and by study of the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

Exemplary embodiments are illustrated in referenced figures. Dimensionsof components and features shown in the figures are generally chosen forconvenience and clarity of presentation and are not necessarily shown toscale. The figures are listed below.

FIG. 1 is a block diagram of an exemplary system for training anartificial neural network, according to one embodiment.

FIG. 2 is combined block diagram/flow chart illustrating a trainingarchitecture and an associated method for training the artificial neuralnetwork of FIG. 1, according to an embodiment.

FIG. 3 is combined block diagram/flow chart illustrating an inferencearchitecture and an associated method for classifying a new raw sensorysignal and generating a new image based on that signal, according to anembodiment.

DETAILED DESCRIPTION

Disclosed herein is an artificial neural network (ANN) that receives araw sensory signal which was acquired by a medical imaging system,predicts a class for the signal, and generates a corresponding imagewhich may be used for review by medical professionals as well as forarchiving. Also disclosed is a method for training such ANN.

By way of example, a medical imaging system, such as an X-Ray machine, aCT scanner, an MRI scanner, a positron emission tomography (PET)scanner, a single photon emission computed tomography (SPECT) scanner,an optical coherence tomography (OCT) imager, an ultrasound imager,etc., may acquire a raw sensory signal respective of a body part of apatient. That signal, without being subject to any image processing, isprovided as input to a suitably-trained ANN. The ANN processes thesignal and produces the following outputs: First, a prediction of aclass to which the signal (or one or more features included in it)likely belongs, such as a class denoting a certain pathology, diagnosis,anatomical parameter, clinical parameter, and/or the like. Another is agenerated image, of a style similar to that commonly produced by thepertinent medical imaging system, in which the sought-after pathologyand/or anatomy are depicted with sufficient clarity and enhancement.This generated image may be used in lieu of the type of image typicallyproduced by existing medical imaging systems, which employ preset imageprocessing steps to generate an image from the raw sensory signal.

Advantageously, these two outputs are produced by the same, single ANN,wherein the image is generated by an intermediate layer of the ANN andthe class prediction by a later (such as the terminating) layer of theANN.

To train this ANN, multiple data pairs may be used as a training set,each of these pairs including a raw sensory signal acquired by a medicalimaging system, and a processed image generated by the medical imagingsystem from that raw sensory signal. Also included in the training setis a classification label for each of the data pairs, which wasattributed to each processed image by a suitable human expert (such as aradiologist, a doctor, or a technician) who professionally reviewed thatprocessed image; that label is used globally for each pair.

The training may include minimization of a global loss which is a sum(optionally a weighted sum of two losses: First, a loss between theclassification labels of the training set, and classificationpredictions made by the ANN. This ensures that the classificationpredictions by the ANN will be commensurate with the human expertclassifications. Second, a similarity loss between the processed imagesand the images generated by the intermediate layer of the ANN. Thisensures that the generated images depict any relevant pathology and/oranatomy with sufficient clarity and enhancement—potentially better thanan image that would have been generated by the medical imaging systemusing standard, preset image processing steps.

A separate ANN may be trained for each imaging modality (e.g., X-Ray,CT, MRI, ultrasound, etc.), and optionally also for each medical imagingscenario (e.g., mammography, cerebral angiography, spinal imaging,etc.), to ensure that the ANN will produce reproductible results whenemployed with that modality and in similar medical imaging scenarios.Therefore, the training set for each ANN may be modality-specific andoptionally also scenario-specific.

Reference is now made to FIG. 1, which shows a block diagram of anexemplary system 100 for training an artificial neural network,according to an embodiment. System 100 may include one or more hardwareprocessor(s) 102, a random-access memory (RAM) 104, and one or morenon-transitory computer-readable storage device(s) 106.

Storage device(s) 106 may have stored thereon program instructionsand/or components configured to operate hardware processor(s) 102. Theprogram instructions may include one or more software modules, such as atraining module 108. The software components may include an operatingsystem having various software components and/or drivers for controllingand managing general system tasks (e.g., memory management, storagedevice control, power management, etc.), and facilitating communicationbetween various hardware and software components.

System 100 may operate by loading instructions of training module 108into RAM 104 as they are being executed by processor(s) 102. Theinstructions of training module 108 may cause system 100 to receive atraining set 110, process it, and output a trained ANN 118. Training set110 may include: raw sensory signals 112 that were acquired by one ormore medical imaging devices and did not undergo image processing;processed images 114 corresponding to the raw sensory signals 112, in aone-to-one relation; and classification labels 116 that are usedglobally for every pair of raw sensory signal and its processed image.

System 100 as described herein is only an exemplary embodiment of thepresent invention, and in practice may be implemented in hardware only,software only, or a combination of both hardware and software. System100 may have more or fewer components and modules than shown, maycombine two or more of the components, or may have a differentconfiguration or arrangement of the components. System 100 may includeany additional component enabling it to function as an operable computersystem, such as a motherboard, data busses, power supply, a networkinterface card, a display, an input device (e.g., keyboard, pointingdevice, touch-sensitive display), etc. (not shown). Moreover, componentsof system 100 may be co-located or distributed, or the system could runas one or more cloud computing “instances,” “containers,” and/or“virtual machines,” as known in the art.

The instructions of training module 108 are now discussed with referenceto the combined block diagram/flow chart of FIG. 2, which illustrates atraining architecture 200 and an associated method for training the ANN,in accordance with an embodiment.

First, training set 110 is obtained, which includes raw sensory signals112, processed images 114, and classification labels 116 (all from FIG.1). Raw sensory signals 112 may each be provided as a digital file whichrepresents, for example, a matrix of pixels (or a grid of points in acartesian coordinate system) and their associated sensed values.Different medical imaging system manufactures may use different formatsfor their raw sensory signals, and all such formats are intended herein.For medical imaging modalities that produce a series of “slices” of athree-dimensional volume (e.g., as in CT and MRI scans) or a video(e.g., as in some uses of ultrasound imaging), each “signal” of rawsensory signals 112 may in fact include a whole series of slices or avideo acquired in a single imaging session of a certain patient;alternatively, such series or video may be separated into theirindividual slices or frames, respectively, for inclusion in training set110.

Processed images 114, in turn, may be provided as digital image filesthat are the result of applying preset image processing adjustments toraw sensory signals 112 by the medical imaging system. Such adjustmentsmay include, for example, brightness, contrast, sharpness, and/or otheradjustments to raw sensory signals 112, intended by the imaging system'smanufacturer to highlight clinically-important features, to de-emphasizeclinically-insignificant features, and/or to otherwise make theseprocessed images 114 more understandable to medical experts, to name afew examples.

As noted above, raw sensory signals 112 and processed images 114 areprovided in the form of data pairs, each including one raw sensorysignal and one processed image generated by the medical imaging systemfrom the raw sensory signal. Classification labels 116 may be generatedby one or more human experts, who review processed images 114 and decidewhich class each of them belongs to, such as a class denoting a certainpathology (or lack thereof), a certain diagnosis (or lack thereof), acertain quantifiable anatomical or clinical parameter (e.g., organsize/texture/location/posture of a bodily feature, behavior of a dynamicorgan such as the heart or a blood vessel), and/or the like. Optionally,there is more than one classification label 116 for each of processedimages 114, such as two, three, or even more labels. Examples ofpossible types of classification labels 116 include: “malignant” or“benign,” “tumor” or “clean,” “normal” or “abnormal,” “stage X” or“stage Y,” “herniated disc” or “bulging disc” or “normal disc,” etc.Those of skill in the art will recognize many other types ofclassification labels that are used in the art for medical images.

Each of classification labels 116 may be used globally for one of thedata pairs, because assuming it is the ground truth for one of processedimages 114—it is also the ground truth for the raw sensory signal fromwhich it was generated.

This training set 110 may then be fed to an ANN 202, such as a deepneural network (DNN) or any other suitable type of an ANN, in a trainingmode: Raw sensory signals 112 may be provided to a first layer 204 ofANN 202, and processed images 114 may be provided to an intermediatelayer 206 of ANN 202. A terminating (last) layer 208 of ANN 202 mayoutput classification predictions 212, which are learned to becommensurate with the provided classification labels 116. ANN 202 isshown here with three layers, but in fact may include one or moreadditional intermediate layers performing various required calculations.

The training of ANN 202 may be conducted as follows: a “global” loss isminimized, wherein this global loss is a sum (optionally a weighted sum)of the following: First, a loss between classification labels 116 andclassification predictions 212 by the DNN. Second, a similarity loss(based on a predefined image-to-image similarity metric) betweenprocessed images 114 and images (not shown in this figure) generated byintermediate layer 206 of ANN 202.

This training yields trained ANN 118 (of FIG. 1), which is now describedin greater detail with reference to FIG. 3. This figure shows a combinedblock diagram/flow chart of an inference architecture 300 and anassociated method for classifying a new raw sensory signal andgenerating a new image based on that signal.

A new raw sensory signal (hereinafter “new signal”) 300 is acquired byanother medical imaging system of the same modality, such as a system inuse at a hospital, clinic, or the like. New signal 300 may then be fedto a first layer 304 of trained ANN 118, which ANN may execute on asystem (not shown, for reasons of conciseness) similar to system 100 ofFIG. 1, but with an inference module in lieu of training module 108.

Next, an intermediate layer 306 of trained ANN 118 generates a new image310 that is highly likely to highlight clinically-important features, tode-emphasize clinically-insignificant features, and/or to otherwise makethis new image more understandable to medical experts—potentially betterthan if the new raw sensory signal were to undergo preset imageprocessing adjustments by the medical imaging system.

Lastly, a terminating (last) layer 308 of trained ANN 118 outputs aclassification prediction 312 for new signal 300.

Discussed now are a number of variations of the above embodiments.

One variation is to include, in the ANN (such as in one or more layersthereof), a generative adversarial network (GAN), or a similar neuralnetwork operating according to GAN principles which are known in theart. Such GAN or similar neural network will then be responsible forgenerating the new images such that their statistics (i.e.,transformation parameters from the new raw sensory signals to the newimages) are as similar as possible to the statistics of the training set(i.e., transformation parameters from the raw sensory signals to theprocessed images).

Another variation is to train the ANN to also segment pathologies,anatomical structures, and/or any other distinct areas, in the images itgenerates. To this end, the training set may additionally includemanually-annotated segmentations of the desired distinct areas appearingin the processed images; for example, segmentations of one or morepathologies (e.g., tumors, growths, foreign objects, inflammation,tissue damage, etc.), and/or anatomical structures (e.g., particularbones, spinal discs, blood vessels, organs, etc.). Then, these manualsegmentations may be used as part of the ground truth of the network,and the global loss minimization may also weigh in a loss ofsegmentations predicted by the ANN, relative to the manually-annotatedsegmentations. This trained ANN will thus be able, given a new rawsensory signal, to also output (from one of its intermediate layers) asegmentation of the pertinent distinct area or an image focused on thatarea.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device havinginstructions recorded thereon, and any suitable combination of theforegoing. A computer readable storage medium, as used herein, is not tobe construed as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire. Rather, the computer readable storage mediumis a non-transient (i.e., not-volatile) medium.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.

The description of a numerical range should be considered to havespecifically disclosed all the possible subranges as well as individualnumerical values within that range. For example, description of a rangefrom 1 to 6 should be considered to have specifically disclosedsubranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4,from 2 to 6, from 3 to 6 etc., as well as individual numbers within thatrange, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of thebreadth of the range.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method comprising operating at least onehardware processor to: obtain a training set which comprises: multipledata pairs each comprising: (i) a raw sensory signal acquired by amedical imaging system, and (ii) a processed image generated by themedical imaging system from the raw sensory signal, and a classificationlabel for each of the data pairs; and based on the training set, trainan artificial neural network (ANN), wherein the training comprisesminimizing a global loss which is a weighted sum of: a loss between theclassification labels and classification predictions by the ANN, and asimilarity loss between the processed images and images generated by anintermediate layer of the ANN, such that the trained ANN is configured,for a new raw sensory signal: to predict a new classification, and togenerate a new image by the intermediate layer of the ANN.
 2. The methodof claim 1, further comprising: acquiring the new raw sensory signal byanother medical imaging system; and applying the trained ANN to the newraw sensory signals, to: predict the new classification for the new rawsensory signal, and generate the new image from the new raw sensorysignal.
 3. The method of claim 1, wherein: the training set furthercomprises manual segmentations of distinct areas for at least some ofthe processed images; and the training is further to segment distinctareas in at least some of the generated new images.
 4. The method ofclaim 3, wherein the distinct areas each represent at least one of: apathology and an anatomical structure.
 5. The method of claim 1, whereinthe ANN comprises a deep neural network (DNN).
 6. The method of claim 1,wherein the ANN comprises a generative adversarial network (GAN).
 7. Asystem comprising: at least one hardware processor; and a non-transitorycomputer-readable storage medium having program code embodied therewith,the program code executable by said at least one hardware processor to:(a) obtain a training set which comprises: multiple data pairs eachcomprising: (i) a raw sensory signal acquired by a medical imagingsystem, and (ii) a processed image generated by the medical imagingsystem from the raw sensory signal, and a classification label for eachof the data pairs, and (b) based on the training set, train anartificial neural network (ANN), wherein the training comprisesminimizing a global loss which is a weighted sum of: a loss between theclassification labels and classification predictions by the ANN, and asimilarity loss between the processed images and images generated by anintermediate layer of the ANN, such that the trained ANN is configured,for a new raw sensory signal: to predict a new classification, and togenerate a new image by the intermediate layer of the ANN.
 8. The systemof claim 7, wherein the program code executable by said at least onehardware processor to: acquire the new raw sensory signal by anothermedical imaging system; and apply the trained ANN to the new raw sensorysignals, to: predict the new classification for the new raw sensorysignal, and generate the new image from the new raw sensory signal. 9.The system of claim 7, wherein: the training set further comprisesmanual segmentations of distinct areas for at least some of theprocessed images; and the training is further to segment distinct areasin at least some of the generated new images.
 10. The system of claim 9,wherein the distinct areas each represent at least one of: a pathologyand an anatomical structure.
 11. The system of claim 7, wherein the ANNcomprises a deep neural network (DNN).
 12. The system of claim 7,wherein the ANN comprises a generative adversarial network (GAN).
 13. Asystem comprising: at least one hardware processor; and a non-transitorycomputer-readable storage medium having program code embodied therewith,the program code executable by said at least one hardware processor to:(a) acquire a new raw sensory signal by a medical imaging system, and(b) apply an artificial neural network (ANN) to the new raw sensorysignal, to: predict a new classification for the new raw sensory signal,and generate, by an intermediate layer of the ANN, a new image from thenew raw sensory signal.
 14. The system of claim 13, wherein: the ANN wastrained based on a training set which comprises: multiple data pairseach comprising: (i) a raw sensory signal acquired by another medicalimaging system, and (ii) a processed image generated by the othermedical imaging system from the raw sensory signal, and a classificationlabel for each of the data pairs, the training of the ANN comprisedminimizing a global loss which is a weighted sum of: a loss between theclassification labels and classification predictions by the ANN, and asimilarity loss between the processed images and images generated by theintermediate layer of the ANN.
 15. The system of claim 14, wherein: thetraining set further comprises manual segmentations of distinct areasfor at least some of the processed images; and the training is furtherto segment distinct areas in at least some of the generated new images.16. The system of claim 15, wherein the distinct areas each represent atleast one of: a pathology and an anatomical structure.
 17. The system ofclaim 13, wherein the ANN comprises a deep neural network (DNN).
 18. Thesystem of claim 13, wherein the ANN comprises a generative adversarialnetwork (GAN).
 19. The system of claim 14, wherein the ANN comprises adeep neural network (DNN).
 20. The system of claim 14, wherein the ANNcomprises a generative adversarial network (GAN).